Long Term Power System Expansion Planning

Transcription

1 Increasing shares of Variable Renewable Energy Long-term planning and flexibility of the electricity system Long Term Power System Expansion Planning Amman, Jordan 28th 29th November 2017

2 1. Scenario Development, Planning, Forecasting 2. Hybrid Systems 2.1 Hybrid Configurator 3. Energy Efficiency and Energy Management 3.1 Smart Energy Hub 4. Techno economic planning, Running interconnected power Systems 2

3 1. Scenario Development, Planning, Forecasting 2. Hybrid Systems 2.1 Hybrid Configurator 3. Energy Efficiency and Energy Management 3.1 Smart Energy Hub 4. Techno economic planning, Running interconnected power Systems 3

4 Basic Management Techniques for Energy Strategies Structured Analysis Procedure Reference Energy System Strategy Management Tableau 4

5 Structured Analysis Procedure (SAP) Identification of Problems to be Solved Objectives Object and Purpose Scope and Limits Frame Data Ref. Energy System Scenarios and Strategies Selection of Methodology and Tools Data Collection Conduct Analysis Development of Policies Decision Planning of Realization Monitoring 5

6 Reference Energy System Methodology Coal Power Plant Emission Distr. Heat Electr. Energy 6

7 Reference Energy System Energy System Boundary Resources Demand, Services 7

8 TRANSPORT HOUSEHOLDS IMPORTS INDUSTRY Reference Energy System (RES) SYSTEM LIMIT Refinery Wholesale Wholesale Wholesale Heat& Power Boiler Gasoline Distrib. Fuel Oil Dirstrib. Distr.Heat. Distrib. Furnace Furnace Gas Electric. DH-Suppl. El. Motor Electricity Process Heat Room Heat Power Pipeline Mining & Process. Mining & Process. Gasturbine Nuclear - Power Coal - Power Hydro - Power Transmiss. Gas Distrib. Electicity Distrib. Elect.Heatg. Oil-Heating Gas-Heating DH-Supply Cars Railways Room Heat Electricity Person km Freight km Legend: Input Process Output Flow 8

9 Scenario Management (Technique) I. Definition of Parameters II. Development scenarios of exogenous factors Low Price Resources / Primary Energy Prices -- High Price Resources / Primary Energy Prices ++ III. Strategies as Options BAU Massive DH Expansion REN Sbg. BAU Massive DH Expansion REN Sbg. IV. Quantitative Results Energy Politics Infrastructure Opt. 9

10 "Robust"Strategies Objectives FRAME DATA Scenario A Scenario B Strategy I Strategy II Strategy III Strategy IV Strategy I Strategy II Strategy III Strategy IV CO2 AI AII AIII AIV BI BII BIII BIV 10 ECU AI AII AIII AIV BI BII BIII BIV Strategy IV is "robust" in all "Cases"

11 11 Thermodynamic Principles

12 1 st Principle of Thermodynamics what comes in-must go out Inputs( t) Outputs( t) O( t) I( t) I O [ MWh] I(t) Power Station P=50 MW Ŋ=0.4 L(t) L O I O(t) O P t O 50[ MW ] 1[ h] 50[ MWh] 75[ MWh] 12

13 2 nd Principle of Thermodynamics T T u C ( T T ) S T T u u 1 T S T T u T C S Entropie 13

14 Energy Terms along the Value Chain Energy Supply Side Energy Demand Side Primary Energy Power Generation Power Transmissio n Final Energy Power Distribution Energy End Users Energy Services Coal Industry Household Appliances

15 Classification of Energy Analysis and Planing Tools I II Category Structure Purpose Method Energy Information System Energy Demand Model III Energy Supply and -System Model IV Integrated Energy- Economy Model Database + Analysis Tool Stand Alone Program Stand Alone or Model Set Stand Alone Program with one Set of Equations V Modular Packages Set of Modules Integrated through a Database Data Storage, Retrieval, Presentation, Processing, Evaluation Analysis of Determining Factors of the Energy Demand and Structure of Energy Requirement Identification of Suitable Energy Supply Structure and Impacts Analysis of the Interaction of the Energy Sector and the Economy Support of the Energy and Environmental Planning Process Relational Database, Hierarchical Database, Network Database Econometrics Process-Engineering Simulation Optimization (LP, Dynamic- Programming, System Dynamics) Simulation, Optimization I/O Econometrics Combination of Different Methodologies CO2-DB MAED, EXCEL MESSAGE, MARKAL-TIMES, LEAP,ESM,WASP, GTMAX ETA-MACRO MESAP, ENPEP 15

16 1. Scenario Development, Planning, Forecasting 2. Hybrid Systems 2.1 Hybrid Configurator 3. Energy Efficiency and Energy Management 3.1 Smart Energy Hub 4. Techno economic planning, Running interconnected power Systems 16

17 Hybrid Power Systems Characteristics of hybrid power system: combination of two or more power generation technologies to complement each other. The combination achieves benefits which the separate technologies could not. Diesel and gas generator Photovoltaic (PV) Wind power Hybrid Power Systems Electrical storage / batteries Existing grid connections Concentrated Solar Thermal - CSP 17

18 Hybrid Power Systems PV / Diesel Most common Hybrid Power Systems: combination of Diesel generator and PV to save fuel. Due to the strongly decreasing costs of PV and rising fuel costs the return on investment for such systems is extremely low (today). Text Text 18

19 Hybrid System Technology PV/Diesel Hybrid Layout Text Typical layout: Diesel Generator PV Plant PV Interface Data Interface Main Controller (Master Text Unit) Optional: battery storage Source: Donauer 19

20 Power [kw] Grid connected PV-Diesel Hybrid System Avoid operation of Diesel engine and save fuel 48 Hour Power Profile of PV/Diesel Hybrid System (example) load Potential PV Power [kw] Load [kw] Real PV Power [kw] Real Diesel Power 1 [kw] Real Diesel Power 2 [kw] Real Diesel Power 3 [kw] Power from Diesel 1 Power from Diesel 2 Power from Diesel 3 PVpotential Power from PV 20 Less Power generation from Diesel when Power generated by PV

21 PV-Battery Hybrid System Provide flattened PV generation profile Grid code requirements: ramp rates must be fulfilled, even with higher shares of PV generation. Solution: Battery storage controls the ramp rates and flattens the generation profile. PV generation + Battery Storage = PV + Storage Output 21

22 PV-Battery Hybrid System Management and shift of PV generation 2. PV generation 4. Provision of energy at peak demands 1. allowed capacity max. power infeed 5. avoided curtailment of PV power Power infeed up to the allowed capacity Provision of power at other times 22

23 Hybrid IPP Dairy farm in Saudi Arabia Project Background Project Background 75,000 cows at C in the Arabian desert Peak electrical demand between MWe Heating and Cooling demand for milk processing Currently powered by multiple 4-8 MW diesel generators Challenge Increasing production and energy demand Fuel price increase Fuel budget limitation Disposal of manure Hybrid Project Solution with storage Fuel saver Increase maximum power output Implementation in stages until

24 Hybrid IPP Dairy farm in Saudi Arabia Diesel Generators Diesel Generator specifications: 8 x 8 MW engines running on HFO 48 MW Back-up generators distributed over Dairy farms running on LFO Objective: Main power supply Active and reactive power control Ramp rate + frequency control Peak power supply Back-up power Generation costs: HFO: 3.1 USct./kWh LFO: 4.9 USct./kWh 24 24

25 Hybrid IPP Dairy farm in Saudi Arabia PV-Plant Objective: Provision of electricity (active + reactive) Increase maximum power output during day Save diesel fuel Specifications: 40 MVA (Stage I) + 50 MVA (Stage II) DC/AC ratio: 1.1 (cos Phi requirement) Interfaces: Switchgear at Main Power Station Estimated generation costs: 1-axis tracked: 4.65 USct./kWh (2016) Fixed mounted: 4.85 USct./kWh (2016) 25 25

26 Hybrid IPP Dairy farm in Saudi Arabia Battery Storage Objective: Increase of maximum power output Provision of reactive power Provision of spinning reserve (bridging the time the diesel generators need to start up) Firming of short-term fluctuations of PV (e.g. clouds, sand storms) until ramp rate capabilities of the diesel generators Load shifting of PV energy production to increase PV utilization Black-start capability Source: A123 Systems Specifications of Storage: Lithium-ion technology 50 MW / 25 MWh (Final Stage) CAPEX assumption: 28 mio. USD (2016) 26 26

27 1. Scenario Development, Planning, Forecasting 2. Hybrid Systems 2.1 Hybrid Configurator 3. Energy Efficiency and Energy Management 3.1 Smart Energy Hub 4. Techno economic planning, Running interconnected power Systems 27

28 Working principle of Hybrid Configurator Simulations, Modelling Hybrid Configurator PV PVSyst Helios Solar Plant CAPEX / OPEX Wind Turbines CAPEX / OPEX Fossil Plant CAPEX / OPEX Wind WindPRO Storage CAPEX /OPEX Load CSP SolPRO Energy Flow Cost Optimization Grid Grid Simulator System Costs 28

29 Hybrid Configurator Input / Output Input Hybrid Configurator Output (Report) Fossil Engines Fuel cost and development Consumption at partial load Prime/max/min power CAPEX/OPEX PV Plant Annual/initial degradation Own consumption PVSyst ref. plant output CAPEX/OPEX Electrical Storage Cycle efficiency Cycle/system life Depth of Discharge Discharge rate Aging CAPEX/OPEX Wind turbines Power curve Roughness of landscape Wind profile CAPEX/OPEX Additional Discount rate Demand development Cost escalation Emission certificate cond. Solar Plant CAPEX / OPEX Storage Wind Turbines CAPEX /OPEX CAPEX / OPEX System Costs Load Fossil Plant CAPEX / OPEX Energy Flow Cost Optimization Optimized Configuration PV Plant (Modules, Inverters, Mounting structure orientation) Wind turbines (type, amount, distance) Electrical Storage (Technology, Capacity, Power) Type and technology of reciprocating engines Technical Saving in Emissions System availability Fossil fuel consumption Share of renewable energies Usage of renewable potential Area required Financial Net Present Value Levelized Costs of Electricity Internal Rate of Return CAPEX distribution OPEX distribution Revenues 29

30 Hybrid Configurator Results PV potential battery state of charge load usage of wind usage of PV all Diesel Gensets combined no operation of Diesel Gensets Example graphs from project on Maldives 30 Hybrid Configurator Results Techno-Economic Optimization Optimized energy system configurations for NPV, LCOE, MIRR, Payback period, fuel savings, emission savings, etc.

31 Hybrid Configurator Results Levelized Cost of electricity [$/kwh] most economic plant configuration Power output of hybrid Plant [MW] Storage Capacity [MWh] 31

32 PV off Assessment of power quality and security of supply of optimum hybrid system configuration Hybrid Configurator Output Economic optimum (hybrid system configuration) Grid Simulation (with Power Factory) Assess power quality and security of supply adjust configuration of Hybrid System if necessary Frequency [Hz]; PV Penetration = 63.8 % Frequency [Hz]; PV Penetration = 80.4 % Frequency [Hz]; PV Penetration = 120 % PV off sole operation of Diesel engines Time [s] Static and dynamic simulations of networks to verify grid stability parameters of optimized PV and wind penetration (DigSilent) Consideration of normal operation conditions (e.g. radiation and load change) and fault operation conditions (e.g. short circuit events, sudden loss of PV and/or wind generation or load) Voltage and frequency check of hybrid network according to ISO

33 Example - Hybrid power and RO plants Diesel engines Modeling of additional reverse osmosis (RO) plants Project Site Reciprocating Engine Plant PV on SWRO Buildings Storage Tank Post-Treatment RO Water Transmission Brine water supply Innovative concept: RO operates with overload to maximize renewable utilization Water storage replaces expensive electrical storage MV Switchgear Pre-Treatment Feedwater Reverse Osmosis Plant Optimization of power plant sizes Electrical storage Solar Plant CAPEX / OPEX Wind Turbines CAPEX / OPEX Diesel Plant CAPEX / OPEX CAPEX/ OPEX Electrical Storage Load of grid Load of RO plant PV Plant (offsite) Wind Farm (offsite) PV wind Energy Flow Cost Optimization Water grid Water storage CAPEX System Costs 33

34 1. Scenario Development, Planning, Forecasting 2. Hybrid Systems 2.1 Hybrid Configurator 3. Energy Efficiency and Energy Management 3.1 Smart Energy Hub 4. Techno economic planning, Running interconnected power Systems 34

35 Measures classification according to the related costs Measures Examples Emphasis No cost teaching, training. switch off when not required repair leaks reschedule loads/usage behaviour of people using existing installed technology at peak energy efficiency Low cost maintenance meters M&T simple controls training end users a combination of investment in low cost technology and people involvement High cost cogeneration heat recovery insulation retrofit with controls energy management systems investment in high cost technology with some people involvement 35

36 Example areas of energy consumption Factory services Industrial Heating Process Building services Motors and Drives Boilers Building shell Compressed Air Low Temperature Processes Air Conditioning and Ventilation Refrigeration High Temperature Processes Lighting Chilled and Cooling Water 36

37 Energy Monitoring & Targeting (M&T) approach Measure energy consumption Energy consumption standard Set targets Comparison : actual - target Reporting Action 1. Measuring the energy consumption of an Energy Accountable Centre (EAC) over a specified period of time 2. Relating energy consumption of each EAC to a measure of output, e. g. production quantity, to define energy consumption standard 3. Setting targets for reduced energy consumption 4. Regularly comparing energy consumption with the target consumption 5. Reporting variances in EAC consumption 6. Taking action to correct variances 37

38 Operation of M&T Agree on consumption targets Analyse and report data and results Take saving actions Repeat and refine 38

39 Model Architecture as base for ISO Energy Management System and Energy Monitoring System Organisation Organisation Processes Procedures and Core Processes Process Components and Services IT Information and Control Flow Process Control, Metering and IT-Systems Energy Building Model and Energy Model 39

40 1. Scenario Development, Planning, Forecasting 2. Hybrid Systems 2.1 Hybrid Configurator 3. Energy Efficiency and Energy Management 3.1 Smart Energy Hub 4. Techno economic planning, Running interconnected power Systems 40

41 Smart Energy Hub Field of Application Operators of Infrastructures Examples Characteristics Energy-Services Data Industries Consumption of energy Reliable, solid Already available Airports Generation of energy Cost efficient Not used Large buildings Procurement of Energy Continuous optimization Cold stores Storage capacities Swimming pools Flexibilities Smart Energy Hub Tool for energy management of infrastructures based on sensor-data and forecasts Energy Market IT /MWh Chance & Risik t t Optimization Automation Cloud-based Real time Price peaks, low level prices Technological progress Complexity, options Knowledge, intelligence Technologies Services 41 make use of chances, avoid risks make manageable make accessible, usable

42 Smart Energy Hub Features Input Output 42 Technical infrastructure Energy: procurement generation storage utilization Data Internal Data: production planning, energy consumption, temperature, passengers, Produktion, External data: forecasts: el. prices, weather,... Visualisation collected data, pattern of system, optimization results assistance for decision making Analysis of data operation pattern, correlations Forecasts e.g. load curves, input for optimization Optimization across different sectors and media output: schedule for plant operation and procurement Simulation changes within existing technical infrastructure, e.g. upgrading

43 Trend towards online optimization From Big Data to optimized operation schedules Identification of interdependencies processes in a system ambient temperature global radiation passengers C Operation chillers boilers temperature level in the terminal C Big Data Collection of time series, e.g. tracking consumption Analysis of correlations (of big data) ambient temperature global radiation number of passengers temperature level in the terminal operation of plants (e.g. boilers) Smart Data Characteristics known e.g. load (heat, cold) depending on weather, number of passengers Optimization of energy supply operation planning: flight schedules, passengers Forecast weather Correlations Plant Data Forecast electricity price Forecast load (internal) Optimization, Control Optimization plant operation Operation schedule (per plant) 43

44 Example 2 Avoidance of natural gas procurement peaks Principle Consumption of natural gas is determined by operation of IC-engine and boilers Boilers can be fired by natural gas or fuel oil Natural gas price is composed by unit rate and capacity price Natural gas consumption can be postponed, brought forward (heat storage) replaced by fuel oil consumption Target Reduction of costs for natural gas procurement peaks(capacity price) 44

45 Last load [kwh] [kw] Example 2 - Avoidance of natural gas procurement peaks Load curve natural gas Cogeneration Plant North Heizwerk-Nord Cogeneration Plant North Last load HW Nord Mittelwert mean value Load curve natural gas cogeneration plant north High natural gas procurement level (nearly 12,000 kw) 45

46 load [kw] Arbeit [kwh] Example 2 - Avoidance of natural gas procurement peaks Optimization fuel oil vs. natural gas consumption Zusammensetzung der Jahresarbeit Composition of optimized fuel consumption aus Gas und Öl Optimization result: cost optimum of fuel oil and natural gas consumption at 7,134 kw Jahresarbeit natural gas Gas Jahresarbeit fuel oil Öl Leistungslimit capacity limit 46

47 Example 2 - Avoidance of natural gas procurement peaks Optimization based on new criteria and determination of optimized operation mode Phase 1: utilization of heat capacity of buildings Early warning, forecasted natural gas peak Gas procurement below limit Phase 3: decreasing temperature level in terminal 1 depending on air traffic and weather Phase 2: fuel switch to fuel oil Phase 1: charging heat storage 47

48 00:00 05:00 10:00 15:00 20:00 01:00 06:00 11:00 16:00 21:00 02:00 07:00 12:00 17:00 22:00 03:00 08:00 13:00 18:00 23:00 04:00 09:00 14:00 19:00 00:00 05:00 10:00 15:00 20:00 01:00 06:00 11:00 16:00 21:00 02:00 07:00 12:00 17:00 22:00 03:00 08:00 13:00 18:00 23:00 04:00 09:00 14:00 19:00 00:00 05:00 10:00 15:00 20:00 01:00 06:00 11:00 16:00 21:00 Lastgang Gas[kW] Example 2 - Avoidance of natural gas procurement peaks Avoidance of natural gas procurement peaks Charging storage, utilization of heat capacity of buildings Fuel switch to fuel oil decrease temperature level where possible peak load bottleneck is noticed early by SEH / operating staff replacement of gas by oil load to be switched switched load limited load of gas Lastgang Gas begrenzt Zu verschiebende Last verschobene Last Jahresarbeit Öl Leistungslimit Grenze Öl (Jahresoptimierung) 48

49 1. Scenario Development, Planning, Forecasting 2. Hybrid Systems 2.1 Hybrid Configurator 3. Energy Efficiency and Energy Management 3.1 Smart Energy Hub 4. Techno economic planning, Running interconnected power Systems 49

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51 Contact Dr. Albrecht Reuter Telefon Mobil Fax Tobias Rehrl Telefon Mobil Fax Internet Internet 51